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Guanyi Chen

7 accepted papers

2024

Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases

COLING 2024main

Theoretical linguists have suggested that some languages (e.g., Chinese and Japanese) are “cooler” than other languages based on the observation that the intended meaning of phrases in these languages depends more on their contexts. As a result, many expressions in these languages are shortened, and…

2024

GPTEval: A Survey on Assessments of ChatGPT and GPT-4

COLING 2024main

The emergence of ChatGPT has generated much speculation in the press about its potential to disrupt social and economic systems. Its astonishing language ability has aroused strong curiosity among scholars about its performance in different domains. There have been many studies evaluating the abilit…

Cited by 125SourcePDFScholar
2024

Intrinsic Task-based Evaluation for Referring Expression Generation

ACL 2024long

Recently, a human evaluation study of Referring Expression Generation (REG) models had an unexpected conclusion: on WEBNLG, Referring Expressions (REs) generated by the state-of-the-art neural models were not only indistinguishable from the REs in WEBNLG but also from the REs generated by a simple r…

2022

Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems

ACL 2022long

In recent years, neural models have often outperformed rule-based and classic Machine Learning approaches in NLG. These classic approaches are now often disregarded, for example when new neural models are evaluated. We argue that they should not be overlooked, since, for some tasks, well-designed no…

2021

Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis

NAACL 2021long

Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execut…

2020

Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation

COLING 2020main

The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences. However, an issue known as posterior collapse (or KL loss vanishing) happens when the VAE is used in text modelling, where the approximate posterior collapses to the prior, and…